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Titlebook: KI 2021: Advances in Artificial Intelligence; 44th German Conferen Stefan Edelkamp,Ralf M?ller,Elmar Rueckert Conference proceedings 2021 S

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41#
發(fā)表于 2025-3-28 16:09:28 | 只看該作者
42#
發(fā)表于 2025-3-28 21:23:48 | 只看該作者
43#
發(fā)表于 2025-3-29 01:31:12 | 只看該作者
Multi-Type-TD-TSR – Extracting Tables from Document Images Using a Multi-stage Pipeline for Table Dechine readable information is rapidly growing. In addition to digitization there is an improvement toward process automation that used to require manual inspection of documents. Although optical character recognition (OCR) technologies mostly solved the task of converting human-readable characters f
44#
發(fā)表于 2025-3-29 04:58:43 | 只看該作者
45#
發(fā)表于 2025-3-29 07:50:01 | 只看該作者
Semantic Segmentation of Aerial Images Using Binary Space Partitioningating and updating maps. However, gathering enough training data to train a proper model for the automated analysis of aerial images is usually too labor-intensive and thus too expensive in most cases. Therefore, domain adaptation techniques are often necessary to be able to adapt existing models or
46#
發(fā)表于 2025-3-29 14:13:27 | 只看該作者
47#
發(fā)表于 2025-3-29 18:38:09 | 只看該作者
Selective Pseudo-Label Clusteringd so produce a lower dimensional representation, which is more amenable to clustering techniques. As clustering is typically performed in a purely unsupervised setting, where no training labels are available, the question then arises as to how the DNN feature extractor can be trained. The most accur
48#
發(fā)表于 2025-3-29 19:47:59 | 只看該作者
49#
發(fā)表于 2025-3-30 02:13:19 | 只看該作者
A Demonstrator for Interactive Image Clustering and Fine-Tuning Neural Networks in Virtual Realityvolutional neural networks (CNNs). We apply dimensionality reduction techniques to project images into the 3D space, where the user can directly interact with the model. The user can change the position of an image by using natural hand gestures. This manipulation triggers additional training steps
50#
發(fā)表于 2025-3-30 05:01:04 | 只看該作者
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